ESS: Expected Simplex Skewness Local Dimension Estimation
In kjohnsson/intrinsicDimension: Intrinsic Dimension Estimation

Description Usage Arguments Details Value Author(s) References Examples

Local intrinsic dimension estimation with the ESS method

1	essLocalDimEst(data, ver, d = 1)

`data`	Local data set for which dimension should be estimated.
`ver`	Possible values: `'a'` and `'b'`. See Johnsson et al. (2015).
`d`	For `ver = 'a'`, any value of `d` is possible, for `ver = 'b'`, only `d = 1` is supported.

The ESS method assumes that the data is local, i.e. that it is a neighborhood taken from a larger data set, such that the curvature and the noise within the neighborhood is relatively small. In the ideal case (no noise, no curvature) this is equivalent to the data being uniformly distributed over a hyper ball.

A DimEst object with two slots:

`dim.est`	The interpolated dimension estimate.
`ess`	The ESS value produced by the algorithm.

Kerstin Johnsson, Lund University

Johnsson, K., Soneson, C., & Fontes, M. (2015). Low Bias Local Intrinsic Dimension Estimation from Expected Simplex Skewness. IEEE Trans. Pattern Anal. Mach. Intell., 37(1), 196-202.